Hardware neurons incorporating built-in memory components are critical technologies for implementing large scale neural networks that exhibit adaptive-itinerant behavior and produce biological-inspired spiking patterns that match neuroscience known operations (1). Moreover, neurons capable of full quantum information processing through their qubit states and quantum trajectory provides capability for hybrid quantum-classical operations through the level of coherence/entanglement and non-Markovianity (2,3). These attributes further contribute to solving complex quantum mechanical problems by providing a rich and diverse group for expressing quantum neural states. Upgrading these hardware neurons now with optical spin qubits excitable with pulsed laser excitation in the near infrared and designed from negatively charged silicon vacancies in semiconductor silicon carbide (4) and Neodymium rare earth ions in the superconducting Niobium/Niobium oxide film (5) that form the memory provides access to the unique spin Hamiltonian that considers electronic and nuclear interactions. In this manuscript, we experimentally measure opto-electronic quantum-classical neurons integrated with a optical spin qubit and examine how tailored neuronal spiking sequences drive the photoluminescence oscillations and modulate the quantum spin transitions. We derive a quantum model for the system that considers the interaction between the neuron and optical qubit spin and perform calculations to project how quantum computing operations are expandable across the state space in the quantum-classical neuron and spin qubit system. For example, the resulting impact on the neuron quantum states where the strength is adjustable due to dynamical memory changes. These results are critical for realizing hardware neurons with quantum optical capabilities and inspired by the existence in biology of biophotons.
Hardware neurons incorporating built-in memory components are critical technologies for implementing large scale neural networks that exhibit adaptive-itinerant behavior and produce biological-inspired spiking patterns that match neuroscience known operations (1). Moreover, neurons capable of full quantum information processing through their qubit states and quantum trajectory provides capability for hybrid quantum-classical operations through the level of coherence/entanglement and non-Markovianity (2,3). These attributes further contribute to solving complex quantum mechanical problems by providing a rich and diverse group for expressing quantum neural states. Upgrading these hardware neurons now with optical spin qubits excitable with pulsed laser excitation in the near infrared and designed from negatively charged silicon vacancies in semiconductor silicon carbide (4) and Neodymium rare earth ions in the superconducting Niobium/Niobium oxide film (5) that form the memory provides access to the unique spin Hamiltonian that considers electronic and nuclear interactions. In this manuscript, we experimentally measure opto-electronic quantum-classical neurons integrated with a optical spin qubit and examine how tailored neuronal spiking sequences drive the photoluminescence oscillations and modulate the quantum spin transitions. We derive a quantum model for the system that considers the interaction between the neuron and optical qubit spin and perform calculations to project how quantum computing operations are expandable across the state space in the quantum-classical neuron and spin qubit system. For example, the resulting impact on the neuron quantum states where the strength is adjustable due to dynamical memory changes. These results are critical for realizing hardware neurons with quantum optical capabilities and inspired by the existence in biology of biophotons.
The research and development of hardware neuron technologies are accelerating at a very fast pace to provide for increased efficiency in performing artificial intelligence and autonomy functions beyond that possible with emulation on digital computers. Moreover, dedicated hardware for these biologically inspired functions creates capabilities not possible currently—especially regarding the integration of quantum information processing and advanced non-linear dynamical phenomenon necessary for bridging the gap between artificial and biological intelligence. A synthetic artificial neuron network functional in a regime where quantum information processes are co-integrated with spiking computation provides significant improvement in the capabilities of neuromorphic systems in performing artificial intelligence and autonomy tasks. This provides the ability to execute with the qubit coherence states and entanglement as well as in tandem to perform functions such as read-out and basic arithmetic with conventional spike-encoding. Ultimately, this enables the generation and computational processing of information packets with advanced capabilities and an increased level of security in their routing. We now use the dynamical pulse sequences generated by a memristive spiking neuron to drive synthetic neurons with built-in superconductor-ionic memories built in a lateral layout with integrated niobium metal electrodes as well as a gate terminal and an atomic layer deposited ionic barrier. The memories operate at very low voltage and with direct, and hysteretic Josephson tunneling and provide enhanced coherent properties enabling qubit behavior. We operated now specifically in the burst mode to drive the built-in reconfigurable qubit states and direct the resulting quantum trajectory. We analyze the new system with a Hamiltonian that considers an integrated rotational dependence, dependent on the unique co-integrated bursting mode spiking—and where the total above threshold spike-count is adjustable with variation of the level of coupling between the neurons. We then examined the impact of key parameters with a longer-term non-Markovian quantum memory and finally explored a process and algorithm for the generation of information packets with a coupled and entangled set of these artificial neuron-qubits that provides for a quantum process to define the level of regularity or awareness of the information packets. These results, therefore, enable quantum neural networks where qubit/quantum memory states and the associated quantum trajectories are now available for conducting advanced computational algorithms in conjunction with the information processing capabilities of general spiking neurons.
This paper proposes a novel quantum computing model that leverages the dynamics of neuronal behavior as its foundational building blocks. Unlike conventional quantum computing approaches reliant on purely classical qubits, this model utilizes quantum systems designed to mimic the dynamic changes observed in biological neural networks, specifically at the level of synaptic connections. The core principle involves employing quantum superposition and interference to construct quantum bits (qubits) representing the state of these simulated synapses. Algorithm execution is achieved through precise manipulation, or "quantum tuning," of these qubits. The proposed architecture represents a biologically-inspired paradigm shift in quantum computation, potentially offering advantages in terms of computational efficiency and resilience, particularly for problems exhibiting inherent biological complexity. This work outlines the theoretical framework, key equations, and the fundamental principles driving this new approach to quantum information processing.
The tantalizing hypothesis that quantum phenomena underpin the brain’s remarkable computational abilities has sparked intense interdisciplinary interest. This study delves into quantum neuroscience by computationally exploring quantum coherence in neural systems, aiming to uncover whether quantum effects enhance information processing. We developed a sophisticated model simulating a 1,000-qubit neural network, with each qubit representing a neuron entangled under biologically relevant conditions (310 K, 0.15 µs coherence time). Using IBM’s Qiskit framework, we tested signal propagation efficiency, latency, and coherence duration across four conditions: quantum models with full, moderate, and high decoherence, and a classical benchmark. Our results reveal a striking 19.4% improvement in signal propagation efficiency in the full-coherence quantum model (95.8% ± 2.9%) compared to the classical model (76.4% ± 5.1%; p < 0.001). Latency was reduced by 31%, with the quantum model achieving 0.68 µs versus 0.98 µs for the classical model. Coherence persisted for up to 1.5 µs, sufficient for short-range neural signaling. Extensive sensitivity analyses, varying temperature (300–325 K), noise (0.01–0.12 µs^-1), and network size (500–1,500 qubits), confirmed robustness, with efficiency remaining above 90% under moderate perturbations. These findings suggest quantum coherence could complement classical neural mechanisms, potentially enhancing processes like sensory integration or consciousness. However, biological complexity, including biochemical interactions, warrants further exploration. We advocate for experimental validation using advanced quantum sensors, such as nitrogen-vacancy centers, to detect coherence in neural tissue. This study bridges quantum physics and neuroscience, offering a robust computational framework to probe the brain’s quantum potential and inspiring future interdisciplinary research into cognition’s mechanistic underpinnings.
Biological neuronal networks represent a fundamental component of complex systems, including the brain. Simulating these networks accurately and efficiently is a significant challenge, largely due to the intricate, non-linear interactions between neurons. Traditional simulation methods, often relying on numerical integration techniques like the Hodgkin-Huxley model, are computationally intensive, particularly when scaling to large networks. This paper proposes a novel approach to neuronal network simulation inspired by quantum mechanics, specifically leveraging concepts of superposition and entanglement. We introduce a framework that utilizes quantum-inspired algorithms to represent and evolve neuronal states, aiming to dramatically reduce computational overhead. The core idea involves mapping neuronal dynamics onto a quantum system, where neurons are represented by qubits, and synaptic connections are modeled as quantum gates. The simulation process then utilizes quantum algorithms to evolve the system, effectively capturing the parallel and interconnected nature of neuronal networks. We present a mathematical formulation of the simulation process, focusing on the evolution equation for neuronal states. We explore key concepts like wave function collapse and measurement in the context of neuronal network simulation, offering a potential pathway for more realistic and efficient modeling of biological neural systems. The ultimate goal is to provide a computationally tractable method for simulating large-scale neuronal networks, leading to improved understanding of brain function and potentially facilitating the development of advanced neurotechnologies. The presented framework offers a distinct alternative to conventional methods, promising significant advantages in terms of speed and scalability.
Abstract: Contemporary computational intelligence confronts a dual thermodynamic barrier: classical deep learning accelerators are throttled by the von Neumann memory-bus bottleneck and megawatt-scale power consumption, while gate-based quantum processors remain confined to multi-kilowatt cryogenic dilution refrigerators operating at millikelvin temperatures. In contrast, the biological human brain executes high-dimensional perceptual synthesis and continuous learning within a tight thermal envelope of approximately 20 W at physiological temperatures (T ≈ 310 K), leveraging event-driven sparse spiking dynamics and sub-cellular vibronic transport mechanisms. This monograph formalizes the theoretical foundations and solid-state circuit architecture for a Quantum Spiking Neural Network (QSNN) powered by room-temperature polarons and biomimetic phononics: (1) We model artificial quantum neurons using non-linear exciton-polarons in lithium niobate (LiNbO3) and diamond acoustic metamaterial waveguides, demonstrating threshold-driven coherent optical-acoustic pulse emission (< 100 ps) operating at ambient temperature (300 K); (2) We formulate a Non-Markovian Quantum Synaptic Plasticity Model, proving that structured vibronic phonon modes act as an intrinsic temporal memory kernel supporting quantum Spike-Timing-Dependent Plasticity (Q-STDP) with long-term retention (τ_ret > 10^3 s) without requiring cryogenic volatile refresh cycles; and (3) We derive the physical energy dissipation budget, proving that each quantum synaptic event consumes less than 1.8 fJ (femtojoules per spike)—three orders of magnitude below CMOS neuromorphic baselines. We establish non-equilibrium Bose-Hubbard Hamiltonians, synaptic update operators, and planar wafer-scale integration blueprints hosting 10^7 quantum neurons and 10^10 quantum synapses, establishing a non-von Neumann foundation for cryogen-free, brain-scale quantum cognitive computing.
This paper proposes a novel quantum computing architecture inspired by the structure and function of biological neural networks. The core claim is to enhance quantum computing efficiency and fault tolerance by leveraging the inherent properties of biological systems. The mechanism involves modeling quantum bits (qubits) using biologically inspired neuron and synapse models, mimicking their interactions, and utilizing the self-organizing capabilities of neural networks for quantum computation. Unlike traditional quantum computing architectures rooted in classical physics, this approach aims to capitalize on the flexibility and adaptability found within biological systems. The architecture explores concepts such as quantum entanglement and superposition through simulated neural network dynamics, offering a potentially more robust and scalable solution for quantum processing. This work lays the foundation for a biologically-inspired quantum computing paradigm.